Genetics 320 covers the principles of heredity and transmission of genetic factors through generations, molecular genetics and biology, fundamentals of inheritance for the case of linked genes, basics of population genetics, principles of inheritance for quantitative traits, and ways by which genetic material is changed. The course covers traditional and contemporary methods of genetic analysis. For delivery of the course, the course was divided into the following modules: classical genetics, molecular genetics, molecular biology, genetics of linked genes, genetics of populations, genetics of complex traits, large scale chromosomal changes, genetic engineering – GMO, and genomics and bioinformatics.This course is taken by undergraduate students of all departments across the College of Agriculture. Approximately, 180 undergraduate students register in this class every fall semester.


Quantitative Genetics 611 covers the principles of inheritance of quantitative traits. Students will be introduced to the concepts of quantitative genetics and how it can be applied to their graduate breeding research for quantitative traits. Students will learn how to put their existing genetics and plant breeding knowledge into a coherent structure for breeding of quantitative traits. A variety of quantitative and statistical genetics research questions will be discussed. Concepts of quantitative genetics will be visualized by simulation in R environment. Students will learn computation in R environment.For delivery of the course, the course was divided into the following modules: R icebreaker and a refresher for probability and statistics, means and variances of populations and heritability, genetics of populations, selection and response to selection, genotype by environment interaction, genetic mapping, genomic prediction, (supplemented in 2017 by: machine learning, big data, and phenomics data workflow).


R is a free environment that performs a wide variety of functions ranging from data manipulation, statistical modeling, and graphics. This course, as refresher, helps students to organize their existing probability and statistics knowledge into a coherent structure, and gain confidence in data analysis and interpretation in agricultural settings in R environment. The topics of this course are: data structure (vectors, matrices, lists, data frames),  data input, output, and handling (subsetting, merging, appending), exploratory statistics (mean, range, variance, median), graphics (scatter plot, bar plot, pie chart), probability distributions (uniform, binomial, normal, t, F, X2, Poisson), statistical inference (Student’s t test, Chi-squared goodness of fit, simple and multiple regression, and analysis of variance).